Abstract
Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or tasks that are not captured by the initial description. We propose a method that allows an agent to automatically extend the abstract description of its skill set upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure computational efficiency. The resulting system is evaluated on a symbolic planning benchmark task and on object rearrangement tasks in simulation. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 25% higher success rate at a 67% faster runtime. Code is available at https://github.com/ethz-asl/high_level_planning.
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